XML Encoding and Web Services for Spatial OLAP Data Cube Exchange: an SOA Approach

XML and Web Services technologies have revolutionized the way data are exchanged on the Internet. Meanwhile, Spatial OLAP (SOLAP) tools have emerged to bridge the gap between the Business Intelligence and Geographic Information Systems domains. While Web Services specifications such as XML for Analysis enable the use of OLAP tools in Service Oriented Architecture (SOA) environments, no solution addresses the exchange of complete SOLAP data cubes (comprising both spatial and descriptive data and metadata) in an interoperable fashion. This paper proposes a new XML grammar for the exchange of SOLAP data cubes, containing both spatial and descriptive data and metadata. It enables the delivery of the cube schema, dimension members (including the geometry of spatial members) and fact data. The use of this XML format is then demonstrated in the context of a Web Service. Such services can be deployed in various situations, not limited to traditional client-server platforms but also ubiquitous mobile computing environments

Easier surveillance of climate-related health vulnerabilities through a Web-based spatial OLAP application

<p>Abstract</p> <p>Background</p> <p>Climate change has a significant impact on population health. Population vulnerabilities depend on several determinants of different types, including biological, psychological, environmental, social and economic ones. Surveillance of climate-related health vulnerabilities must take into account these different factors, their interdependence, as well as their inherent spatial and temporal aspects on several scales, for informed analyses. Currently used technology includes commercial off-the-shelf Geographic Information Systems (GIS) and Database Management Systems with spatial extensions. It has been widely recognized that such OLTP (On-Line Transaction Processing) systems were not designed to support complex, multi-temporal and multi-scale analysis as required above. On-Line Analytical Processing (OLAP) is central to the field known as BI (Business Intelligence), a key field for such decision-support systems. In the last few years, we have seen a few projects that combine OLAP and GIS to improve spatio-temporal analysis and geographic knowledge discovery. This has given rise to SOLAP (Spatial OLAP) and a new research area. This paper presents how SOLAP and climate-related health vulnerability data were investigated and combined to facilitate surveillance.</p> <p>Results</p> <p>Based on recent spatial decision-support technologies, this paper presents a spatio-temporal web-based application that goes beyond GIS applications with regard to speed, ease of use, and interactive analysis capabilities. It supports the multi-scale exploration and analysis of integrated socio-economic, health and environmental geospatial data over several periods. This project was meant to validate the potential of recent technologies to contribute to a better understanding of the interactions between public health and climate change, and to facilitate future decision-making by public health agencies and municipalities in Canada and elsewhere. The project also aimed at integrating an initial collection of geo-referenced multi-scale indicators that were identified by Canadian specialists and end-users as relevant for the surveillance of the public health impacts of climate change. This system was developed in a multidisciplinary context involving researchers, policy makers and practitioners, using BI and web-mapping concepts (more particularly SOLAP technologies), while exploring new solutions for frequent automatic updating of data and for providing contextual warnings for users (to minimize the risk of data misinterpretation). According to the project participants, the final system succeeds in facilitating surveillance activities in a way not achievable with today's GIS. Regarding the experiments on frequent automatic updating and contextual user warnings, the results obtained indicate that these are meaningful and achievable goals but they still require research and development for their successful implementation in the context of surveillance and multiple organizations.</p> <p>Conclusion</p> <p>Surveillance of climate-related health vulnerabilities may be more efficiently supported using a combination of BI and GIS concepts, and more specifically, SOLAP technologies (in that it facilitates and accelerates multi-scale spatial and temporal analysis to a point where a user can maintain an uninterrupted train of thought by focussing on "what" she/he wants (not on "how" to get it) and always obtain instant answers, including to the most complex queries that take minutes or hours with OLTP systems (e.g., aggregated, temporal, comparative)). The developed system respects Newell's cognitive band of 10 seconds when performing knowledge discovery (exploring data, looking for hypotheses, validating models). The developed system provides new operators for easily and rapidly exploring multidimensional data at different levels of granularity, for different regions and epochs, and for visualizing the results in synchronized maps, tables and charts. It is naturally adapted to deal with multiscale indicators such as those used in the surveillance community, as confirmed by this project's end-users.</p

MULTIPLE REPRESENTATION SPATIAL DATABASES

The 70s have witnessed the advent of digital geographic information. Gradually, paper maps have been replaced by digital products. Mapping agencies rapidly started to build spatial databases, each of them being intended to specific ends and without relationships to each other. Such databases multiplied among and within mapping agencies, whic

From Massive Trajectory Data to Traffic Modeling for Better Behavior Prediction in a Usage-Based Insurance Context

Usage-Based Insurance (UBI) is an insurance framework that has made its appearance in the last few years. It allows direct measurement of the traveling of policyholders, hence the growing interest of the industry to better understand driving behaviors. UBI generates large data volumes, from which events can be extracted, like harsh brakes or accelerations. Still, these events are measured without contextual information, which limits their explanatory power. Traffic is one of these types of contextual information that may have great potential, but access to such data remains an issue. Solutions exist, like traffic data from external providers, but for insurance companies that conduct business over large areas, this could result in very large costs. This paper demonstrates that data from insurance companies acquired via UBI can be used to model traffic. A method based on link travel time is proposed and tested on four Canadian cities. Then, routes created with the model are compared with those created using Google Maps. The results show that for 38 routes with an average length of around 5 km, the difference between the travel time of the routes of the proposed model and Google Maps is as small as one second on average

A Methodology for Updating Geographic Databases using Map Versions

This paper addresses issues concerning the exchange and integration of geographic data between producers and users. Once a producer has delivered a geographic database to a user, who then uses it as a reference for specific applications, the database may be updated on both sides. Consequently, the integration of future updates- delivered by the producer-in the user’s geographic database is a complex operation due to possible conflicts between updates performed by both parties. The resulting database may become inconsistent and the user’s added information may be lost. Users therefore need mechanisms to help them in the process of update integration. This paper provides a methodological framework for the updating of geographic databases. It relies on a multi-version GIS, allowing an automatic detection of conflicting updates between two map versions

A Methodology for Updating Geographic Databases using Map Versions

This paper deals with issues related to the exchange and integration of geographic data between producers and users. Once a producer has delivered a geographic database to a user, who uses it as a reference for his specific applications, the database may be updated on both sides. Consequently, the integration of updates - delivered by the producer -in the user's geographic database is a complex operation due to possible conflicts between updates performed by both actors. The resulting database may be in an inconsistent state and user's added information may be lost. Therefore, users need mechanisms to help them in the process of update integration. This paper provides a methodological framework for the updating of geographic databases. It relies on a multiversion GIS, allowing an automatic detection of conflictual updates between two map versions.ou

Identification of Road Network Intersection Types from Vehicle Telemetry Data Using a Convolutional Neural Network

GPS trajectories collected from automotive telematics for insurance purposes go beyond being a collection of points on the map. They are in fact a powerful data source that we can use to extract map and road network properties. While the location of road junctions is readily available, the information about the traffic control element regulating the intersection is typically unknown. However, this information would be helpful, e.g., for contextualizing a driver’s behavior. Our focus is to use a map-matched GPS OBD-dongle dataset provided by a Canadian insurance company to classify intersections into three classes according to the type of traffic control element present: traffic light, stop sign, or no sign. We design a convolutional neural network (CNN) for classifying intersections. The network takes as entries, for a defined number of trips, the speed and the acceleration profiles over each segment of one meter on a window around the intersection. Our method outperforms two other competing approaches, achieving 99% overall accuracy. Furthermore, our CNN model can infer the three classes even with as few as 25 trips

Identification of Road Network Intersection Types from Vehicle Telemetry Data Using a Convolutional Neural Network

GPS trajectories collected from automotive telematics for insurance purposes go beyond being a collection of points on the map. They are in fact a powerful data source that we can use to extract map and road network properties. While the location of road junctions is readily available, the information about the traffic control element regulating the intersection is typically unknown. However, this information would be helpful, e.g., for contextualizing a driver&rsquo;s behavior. Our focus is to use a map-matched GPS OBD-dongle dataset provided by a Canadian insurance company to classify intersections into three classes according to the type of traffic control element present: traffic light, stop sign, or no sign. We design a convolutional neural network (CNN) for classifying intersections. The network takes as entries, for a defined number of trips, the speed and the acceleration profiles over each segment of one meter on a window around the intersection. Our method outperforms two other competing approaches, achieving 99% overall accuracy. Furthermore, our CNN model can infer the three classes even with as few as 25 trips